AUTHOR=Yang Taoqing , Zheng Xia , Xiao Hongwei , Shan Chunhui , Zhang Jikai TITLE=Moisture content online detection system based on multi-sensor fusion and convolutional neural network JOURNAL=Frontiers in Plant Science VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1289783 DOI=10.3389/fpls.2024.1289783 ISSN=1664-462X ABSTRACT=
To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (